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Hierarchical clustering driven by cognitive features

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Abstract

For data sets of arbitrary shapes and densities, the existing clusterings have much space to be improved to obtain better results. In this paper, clustering is considered as a cognitive problem, and cognitive features are of vital importance to clustering. In combination with psychological experiment, we propose three cognitive features of clustering and model them as a flexible similarity measurement. Meanwhile a new clustering framework is put forward to integrate the cognitive features by employing the similarity measurement. The two attractive advantages are its low complexity and fitness for various types of data sets, such as data sets of different shapes and densities. Some synthetic and real data sets are employed to exhibit the superiority of the new clustering algorithm.

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Authors and Affiliations

  1. Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China

    ChunZhong Li

  2. Institute for Information and System Science, Xi’an Jiaotong University, Xi’an, 710049, China

    ChunZhong Li, ZongBen Xu, Chen Qiao & Tao Luo

Authors
  1. ChunZhong Li
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  2. ZongBen Xu
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  3. Chen Qiao
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  4. Tao Luo
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Correspondence to ChunZhong Li.

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Li, C., Xu, Z., Qiao, C. et al. Hierarchical clustering driven by cognitive features. Sci. China Inf. Sci. 57, 1–14 (2014). https://doi.org/10.1007/s11432-013-4858-x

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  • DOI: https://doi.org/10.1007/s11432-013-4858-x

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